Assisting the diagnosis of thyroid diseases with Bayesian-type and SOM-type neural networks making use of routine test data.

Patients with hyperthyroidism sometimes take much time to receive the final diagnosis. To improve patient QOL, simple screening for hyperthyroidism by thyroid non-specialists at the physical check-up is highly expected. Therefore, we applied both Bayesian-type and SOM-type neural networks since we assured the approach useful in analysing thyroid function diagnosis in the previous work. Routine test (14 parameters) data from 66 subjects with a known diagnosis (18 patients with hyperthyroidism and 48 healthy volunteers) were adopted as learning data, and then 142 individuals who also received the same routine tests at the Tohoku University Hospital were screened to predict patients with hyperthyroidism. Both neural networks using 14 parameters predicted several patients as having hyperthyroidism with high probability, including all three hyperthyroid patients diagnosed later by the physician. Further detailed analysis of the routine test parameters that were important for classification found that screening with a set of three parameters (alkaline phosphatase, serum creatinine and total cholesterol) or plus aspartate aminotransferase allowed for quite accurate screening. These results showed that the same neural networks as previous work allows simple screening of patients for hyperthyroidism on the basis of routine test data, and that physicians not specializing in the thyroid can rapidly identify individuals suspected of having hyperthyroidism, to permit a rapid referral for examination and treatment by thyroid specialists.

[1]  D. Coomans,et al.  The application of linear discriminant analysis in the diagnosis of thyroid diseases , 1978 .

[2]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[3]  David Mackay,et al.  Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .

[4]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[5]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[6]  M. Karplus,et al.  Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors. , 1996, Journal of medicinal chemistry.

[7]  V. Çobankara,et al.  Liver tests in hyperthyroidism: effect of antithyroid therapy. , 1997, Journal of clinical gastroenterology.

[8]  K. Sato,et al.  An improvement of neural networks applied to pharmaceutical problems. , 1997, Chemical & pharmaceutical bulletin.

[9]  Victor L. Berardi,et al.  An investigation of neural networks in thyroid function diagnosis , 1998, Health care management science.

[10]  William D. Penny,et al.  Bayesian neural networks for classification: how useful is the evidence framework? , 1999, Neural Networks.

[11]  William D. Penny,et al.  An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers , 1999, Neural Networks.

[12]  Frank R. Burden,et al.  Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks , 2000, J. Chem. Inf. Comput. Sci..

[13]  Junko Kawakami,et al.  Application of a self-organizing map to quantitative structure-activity relationship analysis of carboquinone and benzodiazepine. , 2004, Chemical & pharmaceutical bulletin.

[14]  K. Takano,et al.  Rapid differential diagnosis of Graves' disease and painless thyroiditis using total T3/T4 ratio, TSH, and total alkaline phosphatase activity. , 2005, Endocrine journal.

[15]  K. Hoshi,et al.  An analysis of thyroid function diagnosis using Bayesian-type and SOM-type neural networks. , 2005, Chemical & pharmaceutical bulletin.